Overview

Dataset statistics

Number of variables16
Number of observations5405
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory355.6 B

Variable types

Categorical4
Numeric12

Alerts

SERIES has constant value "EQ" Constant
DATE has a high cardinality: 1081 distinct values High cardinality
OPEN is highly correlated with HIGH and 7 other fieldsHigh correlation
HIGH is highly correlated with OPEN and 7 other fieldsHigh correlation
LOW is highly correlated with OPEN and 7 other fieldsHigh correlation
PREV. CLOSE is highly correlated with OPEN and 7 other fieldsHigh correlation
LTP is highly correlated with OPEN and 7 other fieldsHigh correlation
CLOSE is highly correlated with OPEN and 7 other fieldsHigh correlation
VWAP is highly correlated with OPEN and 7 other fieldsHigh correlation
52W H is highly correlated with OPEN and 8 other fieldsHigh correlation
52W L is highly correlated with OPEN and 8 other fieldsHigh correlation
VOLUME is highly correlated with 52W H and 3 other fieldsHigh correlation
VALUE is highly correlated with VOLUME and 1 other fieldsHigh correlation
NO OF TRADES is highly correlated with VOLUME and 1 other fieldsHigh correlation
OPEN is highly correlated with HIGH and 7 other fieldsHigh correlation
HIGH is highly correlated with OPEN and 7 other fieldsHigh correlation
LOW is highly correlated with OPEN and 7 other fieldsHigh correlation
PREV. CLOSE is highly correlated with OPEN and 7 other fieldsHigh correlation
LTP is highly correlated with OPEN and 7 other fieldsHigh correlation
CLOSE is highly correlated with OPEN and 7 other fieldsHigh correlation
VWAP is highly correlated with OPEN and 7 other fieldsHigh correlation
52W H is highly correlated with OPEN and 7 other fieldsHigh correlation
52W L is highly correlated with OPEN and 7 other fieldsHigh correlation
VOLUME is highly correlated with NO OF TRADESHigh correlation
VALUE is highly correlated with NO OF TRADESHigh correlation
NO OF TRADES is highly correlated with VOLUME and 1 other fieldsHigh correlation
OPEN is highly correlated with HIGH and 7 other fieldsHigh correlation
HIGH is highly correlated with OPEN and 7 other fieldsHigh correlation
LOW is highly correlated with OPEN and 7 other fieldsHigh correlation
PREV. CLOSE is highly correlated with OPEN and 7 other fieldsHigh correlation
LTP is highly correlated with OPEN and 7 other fieldsHigh correlation
CLOSE is highly correlated with OPEN and 7 other fieldsHigh correlation
VWAP is highly correlated with OPEN and 7 other fieldsHigh correlation
52W H is highly correlated with OPEN and 7 other fieldsHigh correlation
52W L is highly correlated with OPEN and 7 other fieldsHigh correlation
VALUE is highly correlated with NO OF TRADESHigh correlation
NO OF TRADES is highly correlated with VALUEHigh correlation
Source.Name is highly correlated with SYMBOL and 1 other fieldsHigh correlation
SYMBOL is highly correlated with Source.Name and 1 other fieldsHigh correlation
SERIES is highly correlated with Source.Name and 1 other fieldsHigh correlation
Source.Name is highly correlated with OPEN and 11 other fieldsHigh correlation
OPEN is highly correlated with Source.Name and 9 other fieldsHigh correlation
HIGH is highly correlated with Source.Name and 9 other fieldsHigh correlation
LOW is highly correlated with Source.Name and 9 other fieldsHigh correlation
PREV. CLOSE is highly correlated with Source.Name and 9 other fieldsHigh correlation
LTP is highly correlated with Source.Name and 9 other fieldsHigh correlation
CLOSE is highly correlated with Source.Name and 9 other fieldsHigh correlation
VWAP is highly correlated with Source.Name and 9 other fieldsHigh correlation
52W H is highly correlated with Source.Name and 9 other fieldsHigh correlation
52W L is highly correlated with Source.Name and 9 other fieldsHigh correlation
VOLUME is highly correlated with Source.Name and 2 other fieldsHigh correlation
VALUE is highly correlated with NO OF TRADESHigh correlation
NO OF TRADES is highly correlated with Source.Name and 3 other fieldsHigh correlation
SYMBOL is highly correlated with Source.Name and 11 other fieldsHigh correlation
Source.Name is uniformly distributed Uniform
DATE is uniformly distributed Uniform
SYMBOL is uniformly distributed Uniform
VALUE has unique values Unique

Reproduction

Analysis started2022-07-31 17:07:30.429024
Analysis finished2022-07-31 17:07:51.678789
Duration21.25 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Source.Name
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size373.8 KiB
SBIN_Data.csv
1081 
WIPRO_Data.csv
1081 
TCS_Data.csv
1081 
TATA_Data.csv
1081 
RELIANCE_Data.csv
1081 

Length

Max length17
Median length14
Mean length13.8
Min length12

Characters and Unicode

Total characters74589
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRELIANCE_Data.csv
2nd rowRELIANCE_Data.csv
3rd rowRELIANCE_Data.csv
4th rowRELIANCE_Data.csv
5th rowRELIANCE_Data.csv

Common Values

ValueCountFrequency (%)
SBIN_Data.csv1081
20.0%
WIPRO_Data.csv1081
20.0%
TCS_Data.csv1081
20.0%
TATA_Data.csv1081
20.0%
RELIANCE_Data.csv1081
20.0%

Length

2022-07-31T22:37:51.738736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T22:37:51.835751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
sbin_data.csv1081
20.0%
wipro_data.csv1081
20.0%
tcs_data.csv1081
20.0%
tata_data.csv1081
20.0%
reliance_data.csv1081
20.0%

Most occurring characters

ValueCountFrequency (%)
a10810
14.5%
s5405
 
7.2%
_5405
 
7.2%
D5405
 
7.2%
t5405
 
7.2%
.5405
 
7.2%
c5405
 
7.2%
v5405
 
7.2%
I3243
 
4.3%
A3243
 
4.3%
Other values (11)19458
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32430
43.5%
Uppercase Letter31349
42.0%
Connector Punctuation5405
 
7.2%
Other Punctuation5405
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D5405
17.2%
I3243
10.3%
A3243
10.3%
T3243
10.3%
R2162
 
6.9%
E2162
 
6.9%
C2162
 
6.9%
S2162
 
6.9%
N2162
 
6.9%
O1081
 
3.4%
Other values (4)4324
13.8%
Lowercase Letter
ValueCountFrequency (%)
a10810
33.3%
s5405
16.7%
t5405
16.7%
c5405
16.7%
v5405
16.7%
Connector Punctuation
ValueCountFrequency (%)
_5405
100.0%
Other Punctuation
ValueCountFrequency (%)
.5405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63779
85.5%
Common10810
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a10810
16.9%
s5405
 
8.5%
D5405
 
8.5%
t5405
 
8.5%
c5405
 
8.5%
v5405
 
8.5%
I3243
 
5.1%
A3243
 
5.1%
T3243
 
5.1%
R2162
 
3.4%
Other values (9)14053
22.0%
Common
ValueCountFrequency (%)
_5405
50.0%
.5405
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII74589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a10810
14.5%
s5405
 
7.2%
_5405
 
7.2%
D5405
 
7.2%
t5405
 
7.2%
.5405
 
7.2%
c5405
 
7.2%
v5405
 
7.2%
I3243
 
4.3%
A3243
 
4.3%
Other values (11)19458
26.1%

DATE
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1081
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size353.8 KiB
11-03-2022
 
5
28-03-2018
 
5
19-11-2020
 
5
21-05-2021
 
5
15-02-2019
 
5
Other values (1076)
5380 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters54050
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16-05-2022
2nd row13-05-2022
3rd row12-05-2022
4th row11-05-2022
5th row10-05-2022

Common Values

ValueCountFrequency (%)
11-03-20225
 
0.1%
28-03-20185
 
0.1%
19-11-20205
 
0.1%
21-05-20215
 
0.1%
15-02-20195
 
0.1%
11-11-20195
 
0.1%
04-10-20195
 
0.1%
23-12-20205
 
0.1%
24-03-20215
 
0.1%
03-04-20205
 
0.1%
Other values (1071)5355
99.1%

Length

2022-07-31T22:37:51.927785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11-03-20225
 
0.1%
03-03-20215
 
0.1%
26-11-20205
 
0.1%
31-03-20215
 
0.1%
12-08-20205
 
0.1%
23-10-20205
 
0.1%
28-08-20185
 
0.1%
08-01-20195
 
0.1%
17-06-20205
 
0.1%
04-06-20185
 
0.1%
Other values (1071)5355
99.1%

Most occurring characters

ValueCountFrequency (%)
013365
24.7%
211995
22.2%
-10810
20.0%
18280
15.3%
82185
 
4.0%
92140
 
4.0%
31310
 
2.4%
41030
 
1.9%
7995
 
1.8%
5985
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number43240
80.0%
Dash Punctuation10810
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013365
30.9%
211995
27.7%
18280
19.1%
82185
 
5.1%
92140
 
4.9%
31310
 
3.0%
41030
 
2.4%
7995
 
2.3%
5985
 
2.3%
6955
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-10810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common54050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013365
24.7%
211995
22.2%
-10810
20.0%
18280
15.3%
82185
 
4.0%
92140
 
4.0%
31310
 
2.4%
41030
 
1.9%
7995
 
1.8%
5985
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII54050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013365
24.7%
211995
22.2%
-10810
20.0%
18280
15.3%
82185
 
4.0%
92140
 
4.0%
31310
 
2.4%
41030
 
1.9%
7995
 
1.8%
5985
 
1.8%

SERIES
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size311.5 KiB
EQ
5405 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10810
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ5405
100.0%

Length

2022-07-31T22:37:52.008731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T22:37:52.085661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
eq5405
100.0%

Most occurring characters

ValueCountFrequency (%)
E5405
50.0%
Q5405
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10810
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E5405
50.0%
Q5405
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10810
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E5405
50.0%
Q5405
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E5405
50.0%
Q5405
50.0%

OPEN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4125
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1132.839315
Minimum151.95
Maximum4033.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:52.159783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum151.95
5-th percentile237.08
Q1326
median606.9
Q31956.5
95-th percentile3210.04
Maximum4033.95
Range3882
Interquartile range (IQR)1630.5

Descriptive statistics

Standard deviation984.9557937
Coefficient of variation (CV)0.8694576364
Kurtosis-0.03213369956
Mean1132.839315
Median Absolute Deviation (MAD)358.3
Skewness1.023041386
Sum6122996.5
Variance970137.9154
MonotonicityNot monotonic
2022-07-31T22:37:52.260725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28511
 
0.2%
2689
 
0.2%
2549
 
0.2%
20858
 
0.1%
20608
 
0.1%
2758
 
0.1%
2908
 
0.1%
2837
 
0.1%
19807
 
0.1%
21747
 
0.1%
Other values (4115)5323
98.5%
ValueCountFrequency (%)
151.951
< 0.1%
1521
< 0.1%
152.41
< 0.1%
1531
< 0.1%
153.651
< 0.1%
156.11
< 0.1%
157.51
< 0.1%
159.451
< 0.1%
163.11
< 0.1%
1641
< 0.1%
ValueCountFrequency (%)
4033.951
< 0.1%
40121
< 0.1%
3992.71
< 0.1%
39781
< 0.1%
39301
< 0.1%
39252
< 0.1%
39201
< 0.1%
39181
< 0.1%
39101
< 0.1%
39001
< 0.1%

HIGH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4556
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1146.285708
Minimum153.2
Maximum4043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:52.387738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum153.2
5-th percentile239.66
Q1331.35
median614
Q31978
95-th percentile3230.8
Maximum4043
Range3889.8
Interquartile range (IQR)1646.65

Descriptive statistics

Standard deviation994.7326818
Coefficient of variation (CV)0.8677877383
Kurtosis-0.04414565517
Mean1146.285708
Median Absolute Deviation (MAD)362.3
Skewness1.019054466
Sum6195674.25
Variance989493.1082
MonotonicityNot monotonic
2022-07-31T22:37:52.489793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2886
 
0.1%
3655
 
0.1%
3205
 
0.1%
2895
 
0.1%
2985
 
0.1%
2625
 
0.1%
2955
 
0.1%
21655
 
0.1%
293.85
 
0.1%
5254
 
0.1%
Other values (4546)5355
99.1%
ValueCountFrequency (%)
153.21
< 0.1%
155.251
< 0.1%
155.61
< 0.1%
156.151
< 0.1%
157.851
< 0.1%
160.81
< 0.1%
161.91
< 0.1%
162.41
< 0.1%
166.41
< 0.1%
168.251
< 0.1%
ValueCountFrequency (%)
40431
< 0.1%
4041.71
< 0.1%
40121
< 0.1%
3989.91
< 0.1%
3981.751
< 0.1%
39801
< 0.1%
39781
< 0.1%
39771
< 0.1%
39451
< 0.1%
3944.41
< 0.1%

LOW
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4614
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1118.388936
Minimum149.45
Maximum3980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:52.603730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum149.45
5-th percentile234.26
Q1322.1
median598
Q31930.4
95-th percentile3180.8
Maximum3980
Range3830.55
Interquartile range (IQR)1608.3

Descriptive statistics

Standard deviation974.7068185
Coefficient of variation (CV)0.8715275938
Kurtosis-0.02060556082
Mean1118.388936
Median Absolute Deviation (MAD)352
Skewness1.027759066
Sum6044892.2
Variance950053.382
MonotonicityNot monotonic
2022-07-31T22:37:52.706725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2807
 
0.1%
3116
 
0.1%
2556
 
0.1%
19855
 
0.1%
258.15
 
0.1%
2705
 
0.1%
3585
 
0.1%
3005
 
0.1%
3234
 
0.1%
246.64
 
0.1%
Other values (4604)5353
99.0%
ValueCountFrequency (%)
149.451
< 0.1%
150.21
< 0.1%
150.81
< 0.1%
151.151
< 0.1%
151.51
< 0.1%
152.41
< 0.1%
1551
< 0.1%
155.21
< 0.1%
156.71
< 0.1%
159.41
< 0.1%
ValueCountFrequency (%)
39801
< 0.1%
3962.31
< 0.1%
3910.51
< 0.1%
39001
< 0.1%
3892.11
< 0.1%
38661
< 0.1%
38611
< 0.1%
3860.051
< 0.1%
38571
< 0.1%
38561
< 0.1%

PREV. CLOSE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4843
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131.404746
Minimum150.85
Maximum4019.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:52.819730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum150.85
5-th percentile237.06
Q1326.15
median605.6
Q31953.7
95-th percentile3200.13
Maximum4019.15
Range3868.3
Interquartile range (IQR)1627.55

Descriptive statistics

Standard deviation984.1283756
Coefficient of variation (CV)0.86982875
Kurtosis-0.02914544749
Mean1131.404746
Median Absolute Deviation (MAD)357.5
Skewness1.024434972
Sum6115242.65
Variance968508.6596
MonotonicityNot monotonic
2022-07-31T22:37:52.922747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286.45
 
0.1%
285.055
 
0.1%
276.24
 
0.1%
326.74
 
0.1%
2484
 
0.1%
282.854
 
0.1%
285.34
 
0.1%
287.74
 
0.1%
273.33
 
0.1%
261.653
 
0.1%
Other values (4833)5365
99.3%
ValueCountFrequency (%)
150.851
< 0.1%
151.41
< 0.1%
151.951
< 0.1%
152.81
< 0.1%
153.41
< 0.1%
155.31
< 0.1%
158.21
< 0.1%
158.61
< 0.1%
161.31
< 0.1%
162.351
< 0.1%
ValueCountFrequency (%)
4019.151
< 0.1%
3990.61
< 0.1%
3968.151
< 0.1%
3954.551
< 0.1%
3935.651
< 0.1%
3915.91
< 0.1%
3914.651
< 0.1%
3903.31
< 0.1%
3897.91
< 0.1%
3892.91
< 0.1%

LTP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4529
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131.992877
Minimum151.1
Maximum4025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:53.070654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum151.1
5-th percentile237.16
Q1326.05
median605.5
Q31955
95-th percentile3200.28
Maximum4025
Range3873.9
Interquartile range (IQR)1628.95

Descriptive statistics

Standard deviation984.5162875
Coefficient of variation (CV)0.8697195076
Kurtosis-0.03094494906
Mean1131.992877
Median Absolute Deviation (MAD)357.4
Skewness1.023829071
Sum6118421.5
Variance969272.3204
MonotonicityNot monotonic
2022-07-31T22:37:53.203656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2677
 
0.1%
4337
 
0.1%
2606
 
0.1%
5155
 
0.1%
3245
 
0.1%
284.55
 
0.1%
22505
 
0.1%
21125
 
0.1%
286.555
 
0.1%
20005
 
0.1%
Other values (4519)5350
99.0%
ValueCountFrequency (%)
151.11
< 0.1%
151.61
< 0.1%
1521
< 0.1%
153.051
< 0.1%
153.61
< 0.1%
155.451
< 0.1%
157.91
< 0.1%
158.61
< 0.1%
161.31
< 0.1%
161.81
< 0.1%
ValueCountFrequency (%)
40251
< 0.1%
3993.951
< 0.1%
3965.351
< 0.1%
39501
< 0.1%
39431
< 0.1%
39171
< 0.1%
3914.71
< 0.1%
3903.051
< 0.1%
38982
< 0.1%
38841
< 0.1%

CLOSE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4842
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131.93235
Minimum150.85
Maximum4019.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:53.324653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum150.85
5-th percentile237.06
Q1326.2
median605.6
Q31954.05
95-th percentile3200.23
Maximum4019.15
Range3868.3
Interquartile range (IQR)1627.85

Descriptive statistics

Standard deviation984.4674775
Coefficient of variation (CV)0.8697228927
Kurtosis-0.03116874752
Mean1131.93235
Median Absolute Deviation (MAD)357.6
Skewness1.023747259
Sum6118094.35
Variance969176.2143
MonotonicityNot monotonic
2022-07-31T22:37:53.428737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
285.055
 
0.1%
286.45
 
0.1%
285.34
 
0.1%
287.74
 
0.1%
282.854
 
0.1%
276.24
 
0.1%
326.74
 
0.1%
2484
 
0.1%
261.93
 
0.1%
265.753
 
0.1%
Other values (4832)5365
99.3%
ValueCountFrequency (%)
150.851
< 0.1%
151.41
< 0.1%
151.951
< 0.1%
152.81
< 0.1%
153.41
< 0.1%
155.31
< 0.1%
158.21
< 0.1%
158.61
< 0.1%
161.31
< 0.1%
162.351
< 0.1%
ValueCountFrequency (%)
4019.151
< 0.1%
3990.61
< 0.1%
3968.151
< 0.1%
3954.551
< 0.1%
3935.651
< 0.1%
3915.91
< 0.1%
3914.651
< 0.1%
3903.31
< 0.1%
3897.91
< 0.1%
3892.91
< 0.1%

VWAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5286
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1132.543406
Minimum151.82
Maximum4010.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:53.541724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum151.82
5-th percentile237.192
Q1326.36
median605.74
Q31952.66
95-th percentile3206.504
Maximum4010.33
Range3858.51
Interquartile range (IQR)1626.3

Descriptive statistics

Standard deviation984.8408792
Coefficient of variation (CV)0.8695833413
Kurtosis-0.03231240056
Mean1132.543406
Median Absolute Deviation (MAD)357.26
Skewness1.023334018
Sum6121397.11
Variance969911.5574
MonotonicityNot monotonic
2022-07-31T22:37:53.651733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
279.143
 
0.1%
424.323
 
0.1%
270.133
 
0.1%
250.073
 
0.1%
260.672
 
< 0.1%
1115.072
 
< 0.1%
312.152
 
< 0.1%
2043.132
 
< 0.1%
342.862
 
< 0.1%
265.472
 
< 0.1%
Other values (5276)5381
99.6%
ValueCountFrequency (%)
151.821
< 0.1%
151.91
< 0.1%
152.991
< 0.1%
154.021
< 0.1%
154.91
< 0.1%
155.741
< 0.1%
157.741
< 0.1%
159.321
< 0.1%
159.381
< 0.1%
163.71
< 0.1%
ValueCountFrequency (%)
4010.331
< 0.1%
4009.821
< 0.1%
3947.831
< 0.1%
3937.161
< 0.1%
3931.951
< 0.1%
3929.41
< 0.1%
3924.251
< 0.1%
3908.951
< 0.1%
3897.841
< 0.1%
3896.961
< 0.1%

52W H
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct345
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1366.202303
Minimum276.15
Maximum4043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:54.105653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum276.15
5-th percentile334
Q1388.95
median782.5
Q32296.2
95-th percentile3674.8
Maximum4043
Range3766.85
Interquartile range (IQR)1907.25

Descriptive statistics

Standard deviation1135.910053
Coefficient of variation (CV)0.8314362009
Kurtosis-0.4255731308
Mean1366.202303
Median Absolute Deviation (MAD)448.5
Skewness0.9306345768
Sum7384323.45
Variance1290291.648
MonotonicityNot monotonic
2022-07-31T22:37:54.220653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
793250
 
4.6%
388.95248
 
4.6%
3674.8246
 
4.6%
373.8246
 
4.6%
2369.35240
 
4.4%
2296.2207
 
3.8%
351.3203
 
3.8%
1534.5185
 
3.4%
1664.9149
 
2.8%
739.85144
 
2.7%
Other values (335)3287
60.8%
ValueCountFrequency (%)
276.1511
 
0.2%
281.67
 
0.1%
285.65
 
0.1%
286.81
 
< 0.1%
2885
 
0.1%
290.830
0.6%
298.451
 
< 0.1%
300.755
 
0.1%
301.663
1.2%
308.551
 
< 0.1%
ValueCountFrequency (%)
404380
1.5%
3989.968
1.3%
3981.7516
 
0.3%
39801
 
< 0.1%
3896.51
 
< 0.1%
3877.65
 
0.1%
3859.152
 
< 0.1%
3816.71
 
< 0.1%
3804.11
 
< 0.1%
3740.351
 
< 0.1%

52W L
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct271
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean778.7434598
Minimum149.45
Maximum3036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:54.342736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum149.45
5-th percentile159.4
Q1245.05
median412.6
Q31143
95-th percentile2214.95
Maximum3036
Range2886.55
Interquartile range (IQR)897.95

Descriptive statistics

Standard deviation714.4705418
Coefficient of variation (CV)0.9174658649
Kurtosis0.3068618772
Mean778.7434598
Median Absolute Deviation (MAD)253.2
Skewness1.155422683
Sum4209108.4
Variance510468.1552
MonotonicityNot monotonic
2022-07-31T22:37:54.436736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
875.65254
 
4.7%
250.85252
 
4.7%
159.4251
 
4.6%
149.45250
 
4.6%
1506.05250
 
4.6%
1711.15248
 
4.6%
232.35247
 
4.6%
779.1185
 
3.4%
253.5175
 
3.2%
1830133
 
2.5%
Other values (261)3160
58.5%
ValueCountFrequency (%)
149.45250
4.6%
150.25
 
0.1%
151.152
 
< 0.1%
152.41
 
< 0.1%
1551
 
< 0.1%
159.4251
4.6%
160.854
 
0.1%
163.355
 
0.1%
1656
 
0.1%
166.11
 
< 0.1%
ValueCountFrequency (%)
30366
 
0.1%
300432
0.6%
2987.059
 
0.2%
2901.84
 
0.1%
288039
0.7%
28455
 
0.1%
27855
 
0.1%
27555
 
0.1%
2706.155
 
0.1%
2624.4510
 
0.2%

VOLUME
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5403
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13832333.99
Minimum142541
Maximum214955688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:54.546813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum142541
5-th percentile1724797.6
Q14080283
median8009017
Q316244724
95-th percentile47631032.6
Maximum214955688
Range214813147
Interquartile range (IQR)12164441

Descriptive statistics

Standard deviation17402816.85
Coefficient of variation (CV)1.258125843
Kurtosis20.05581883
Mean13832333.99
Median Absolute Deviation (MAD)4930624
Skewness3.60827038
Sum7.47637652 × 1010
Variance3.028580344 × 1014
MonotonicityNot monotonic
2022-07-31T22:37:54.677882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37555072
 
< 0.1%
179722072
 
< 0.1%
55721771
 
< 0.1%
77079531
 
< 0.1%
63112271
 
< 0.1%
124347451
 
< 0.1%
31654951
 
< 0.1%
202499091
 
< 0.1%
64312441
 
< 0.1%
84145141
 
< 0.1%
Other values (5393)5393
99.8%
ValueCountFrequency (%)
1425411
< 0.1%
1445301
< 0.1%
2244211
< 0.1%
2988191
< 0.1%
3153761
< 0.1%
3289911
< 0.1%
4138711
< 0.1%
4565411
< 0.1%
5645091
< 0.1%
5768531
< 0.1%
ValueCountFrequency (%)
2149556881
< 0.1%
2013251761
< 0.1%
1928107721
< 0.1%
1578208821
< 0.1%
1557031401
< 0.1%
1517504211
< 0.1%
1492459731
< 0.1%
1459031021
< 0.1%
1452034391
< 0.1%
1430831961
< 0.1%

VALUE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct5405
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9375572682
Minimum46436853
Maximum1.47 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:54.791813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum46436853
5-th percentile918073969.8
Q13955927032
median6757642220
Q31.159632556 × 1010
95-th percentile2.621089946 × 1010
Maximum1.47 × 1011
Range1.469535631 × 1011
Interquartile range (IQR)7640398529

Descriptive statistics

Standard deviation9629199823
Coefficient of variation (CV)1.027051909
Kurtosis30.29905043
Mean9375572682
Median Absolute Deviation (MAD)3428755458
Skewness4.0523113
Sum5.067497035 × 1013
Variance9.272148924 × 1019
MonotonicityNot monotonic
2022-07-31T22:37:54.913884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.356160157 × 10101
 
< 0.1%
1.80245977 × 10101
 
< 0.1%
47292279021
 
< 0.1%
91441239891
 
< 0.1%
54319957751
 
< 0.1%
44615051351
 
< 0.1%
75306022191
 
< 0.1%
1.566004488 × 10101
 
< 0.1%
53028499831
 
< 0.1%
68453414761
 
< 0.1%
Other values (5395)5395
99.8%
ValueCountFrequency (%)
464368531
< 0.1%
800823261
< 0.1%
1921421511
< 0.1%
2401315051
< 0.1%
2476930131
< 0.1%
2591053111
< 0.1%
2795183931
< 0.1%
2807141851
< 0.1%
2875604841
< 0.1%
2897815911
< 0.1%
ValueCountFrequency (%)
1.47 × 10111
< 0.1%
1.27 × 10111
< 0.1%
1.24 × 10111
< 0.1%
1.18 × 10111
< 0.1%
9.179980463 × 10101
< 0.1%
8.912049492 × 10101
< 0.1%
8.839332015 × 10101
< 0.1%
8.835029614 × 10101
< 0.1%
8.750594271 × 10101
< 0.1%
8.549082574 × 10101
< 0.1%

NO OF TRADES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5366
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174616.0834
Minimum2593
Maximum1428490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size42.4 KiB
2022-07-31T22:37:55.027886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2593
5-th percentile43213.8
Q196681
median143925
Q3216493
95-th percentile412247.8
Maximum1428490
Range1425897
Interquartile range (IQR)119812

Descriptive statistics

Standard deviation125243.3489
Coefficient of variation (CV)0.7172497883
Kurtosis11.21579501
Mean174616.0834
Median Absolute Deviation (MAD)55773
Skewness2.498468505
Sum943799931
Variance1.568589644 × 1010
MonotonicityNot monotonic
2022-07-31T22:37:55.136884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310572
 
< 0.1%
1389672
 
< 0.1%
1361332
 
< 0.1%
910942
 
< 0.1%
1063252
 
< 0.1%
769112
 
< 0.1%
778742
 
< 0.1%
1628702
 
< 0.1%
1759762
 
< 0.1%
1707962
 
< 0.1%
Other values (5356)5385
99.6%
ValueCountFrequency (%)
25931
< 0.1%
65331
< 0.1%
78921
< 0.1%
91901
< 0.1%
94121
< 0.1%
95671
< 0.1%
118311
< 0.1%
123771
< 0.1%
125761
< 0.1%
131451
< 0.1%
ValueCountFrequency (%)
14284901
< 0.1%
12855331
< 0.1%
12330531
< 0.1%
11940591
< 0.1%
11552361
< 0.1%
11549591
< 0.1%
11219591
< 0.1%
10780971
< 0.1%
9909351
< 0.1%
9082281
< 0.1%

SYMBOL
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size331.6 KiB
RELIANCE
1081 
TATASTEEL
1081 
SBIN
1081 
WIPRO
1081 
TCS
1081 

Length

Max length9
Median length5
Mean length5.8
Min length3

Characters and Unicode

Total characters31349
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRELIANCE
2nd rowRELIANCE
3rd rowRELIANCE
4th rowRELIANCE
5th rowRELIANCE

Common Values

ValueCountFrequency (%)
RELIANCE1081
20.0%
TATASTEEL1081
20.0%
SBIN1081
20.0%
WIPRO1081
20.0%
TCS1081
20.0%

Length

2022-07-31T22:37:55.253896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-31T22:37:55.349885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reliance1081
20.0%
tatasteel1081
20.0%
sbin1081
20.0%
wipro1081
20.0%
tcs1081
20.0%

Most occurring characters

ValueCountFrequency (%)
E4324
13.8%
T4324
13.8%
I3243
10.3%
A3243
10.3%
S3243
10.3%
R2162
6.9%
L2162
6.9%
N2162
6.9%
C2162
6.9%
B1081
 
3.4%
Other values (3)3243
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter31349
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E4324
13.8%
T4324
13.8%
I3243
10.3%
A3243
10.3%
S3243
10.3%
R2162
6.9%
L2162
6.9%
N2162
6.9%
C2162
6.9%
B1081
 
3.4%
Other values (3)3243
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin31349
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E4324
13.8%
T4324
13.8%
I3243
10.3%
A3243
10.3%
S3243
10.3%
R2162
6.9%
L2162
6.9%
N2162
6.9%
C2162
6.9%
B1081
 
3.4%
Other values (3)3243
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII31349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E4324
13.8%
T4324
13.8%
I3243
10.3%
A3243
10.3%
S3243
10.3%
R2162
6.9%
L2162
6.9%
N2162
6.9%
C2162
6.9%
B1081
 
3.4%
Other values (3)3243
10.3%

Interactions

2022-07-31T22:37:49.636725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:31.218105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:33.085557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:34.807272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:36.365046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:37.971689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:39.794293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:41.396446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:43.082333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:44.662986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:46.473235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:48.184615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:49.746726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:31.376397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:33.208928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:34.948123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:36.485472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:38.110482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:39.918245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:41.571424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:43.273164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:44.803995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:46.597213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:48.306651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:49.863695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:44.925995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:40.183952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:45.040086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:46.838460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:48.558578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:50.137657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:35.343575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:50.419655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:32.202087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:42.951208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-07-31T22:37:46.280235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:48.074858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-31T22:37:49.517656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-07-31T22:37:55.455813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-31T22:37:55.628885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-31T22:37:55.785885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-31T22:37:55.937890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-31T22:37:56.042812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-31T22:37:51.330751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-31T22:37:51.584654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Source.NameDATESERIESOPENHIGHLOWPREV. CLOSELTPCLOSEVWAP52W H52W LVOLUMEVALUENO OF TRADESSYMBOL
0RELIANCE_Data.csv16-05-2022EQ2434.452481.002416.652426.602428.052427.202444.282856.151930.462015941.515841e+10244925RELIANCE
1RELIANCE_Data.csv13-05-2022EQ2426.002478.002415.352399.402431.452426.602451.672856.151906.089109982.184680e+10408746RELIANCE
2RELIANCE_Data.csv12-05-2022EQ2427.502434.852370.002449.302403.502399.402400.602856.151906.094562802.270075e+10359540RELIANCE
3RELIANCE_Data.csv11-05-2022EQ2472.652484.952421.952474.652450.752449.302454.292856.151906.076811571.885176e+10325039RELIANCE
4RELIANCE_Data.csv10-05-2022EQ2495.002526.602458.002518.302461.702474.652495.142856.151906.090046362.246785e+10329083RELIANCE
5RELIANCE_Data.csv09-05-2022EQ2574.952597.102507.002620.652508.002518.302540.752856.151906.083456492.120422e+10344258RELIANCE
6RELIANCE_Data.csv06-05-2022EQ2612.202659.002593.552640.902628.002620.652619.882856.151906.090684482.375828e+10291431RELIANCE
7RELIANCE_Data.csv05-05-2022EQ2723.502730.002632.002693.652643.002640.902677.812856.151906.079427212.126910e+10256514RELIANCE
8RELIANCE_Data.csv04-05-2022EQ2785.002790.002676.302780.452692.002693.652728.032856.151906.088827922.423248e+10277638RELIANCE
9RELIANCE_Data.csv02-05-2022EQ2762.002805.502758.052790.252780.902780.452783.292856.151906.043690221.216027e+10189251RELIANCE

Last rows

Source.NameDATESERIESOPENHIGHLOWPREV. CLOSELTPCLOSEVWAP52W H52W LVOLUMEVALUENO OF TRADESSYMBOL
5395WIPRO_Data.csv12-01-2018EQ321.00323.75317.20321.10320.00318.80319.36568.0252.031626641.010016e+0931980WIPRO
5396WIPRO_Data.csv11-01-2018EQ323.55326.05319.00326.70320.05321.10322.79568.0252.021164626.831816e+0824600WIPRO
5397WIPRO_Data.csv10-01-2018EQ316.40327.85315.10317.20326.40326.70322.70568.0252.038111381.229866e+0936772WIPRO
5398WIPRO_Data.csv09-01-2018EQ312.60320.00306.40311.15315.30317.20313.19568.0252.045755721.433025e+0946168WIPRO
5399WIPRO_Data.csv08-01-2018EQ310.00312.90309.10309.55311.80311.15310.76568.0252.017852485.547758e+0826106WIPRO
5400WIPRO_Data.csv05-01-2018EQ313.00313.90307.70311.65310.05309.55311.03568.0252.016132055.017510e+0823732WIPRO
5401WIPRO_Data.csv04-01-2018EQ310.10313.00307.45309.95310.60311.65309.71568.0252.014645844.535995e+0816466WIPRO
5402WIPRO_Data.csv03-01-2018EQ320.40320.40308.75318.70309.85309.95315.06568.0252.021976776.923997e+0842609WIPRO
5403WIPRO_Data.csv02-01-2018EQ315.85324.00314.45316.55316.50318.70318.70568.0252.028745189.160983e+0829055WIPRO
5404WIPRO_Data.csv01-01-2018EQ311.50320.00309.45314.25314.80316.55316.44568.0252.033503971.060197e+0927501WIPRO